Motion generation for humanoid robots with automatically derived behaviors

Abstract

In this paper, we present a method for motion generation from automatically derived behaviors for a humanoid robot. Behaviors are derived automatically by using the underlying spatio-temporal structure in motion. The derived behaviors are stored in a robot's long-term (or procedural) memory. New motions are generated from the derived ones with a search mechanism. In our approach, vision, speech recognition, short-term memory and decision-making operate in parallel with long-term memory in a unique architecture. This organization is intended for autonomous robot control and learning.